GitLab

* Change Byte to Bool for training masks

* Check if module has Bool, otherwise use Byte

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Fix

* Fix

* Update

* Fix style

* Update

* Update

* Fix

* Update

* Update

* Refactor

* Add note

* Update

* CI

* Update

* Update

* rrn model and sudoku

* add README

* refine the code, add doc strings

* add sudoku solver

* add example for sudoku_solver

* ggnn example

* Rewrite README file

* fix typos

* convert np.ndarray to backend tensor

* add datasets

* add qm7

* add dataset

* add dataset

* fix

* change ppi

* tu dataset

* add datasets

* fix

* fix

* fix

* fix

* add docstring

* docs

* doc

* new hetero RGCN

* bgs running

* fix gpu

* am dataset

* fix bug in label preparation

* Fix AM training; add result

* rm sym link

* new embed layer; mutag

* mutag matched; other fix

* minor fix

* dataset refactor

* new data loading

* rm old files

* refactor

* docstring

* include literal nodes in AIFB dataset

* address comments

* docstring

* initial commit of cluster GCN

* update readme

* fix small bugs running ppi

* nearly sota ppi training script&update readme

* rm unused imports&add shebang line to scripts

* minor comments&readme appended

* add rnd seed control&update readme

* Add HAN

* Fix

* WIP; load raw ACM dataset

* DGL's own preprocessing with metapath coalescer

* various fixes

* comparison against simple logistic regression

* rename

* fix test

* init

* rm data

* README

* fix rating values that are not decimal

* rm stale codes

* small fix

* upd

* rewrite decoder

* fix many

* many fix; performance matched

* upd; handle sparse input

* upd

* address comments

* more docstring; download data automatically

* shared param mode

* metapath sampler

* lint & fixes

* lint x2

* lint x3

* fix windows

* remove max_cycle argument

* add todo note

* update

* speedup

* add some comments

* Update

* Update

* Update

* Update

* Update

* upd

* add test

* fix

* upd

* merge

* hotfix

* upd

* fix

* Update

* Update

* Update

* Update fix

* Update

* Update

* Refactor

* Update

* Update

* Update

* Update

* Update

* Update

* Fix style

* upd

* fig edgebatch edges

* add test

* trigger

* upd

* Add serialization

* fix gin

* Add serialization

* fix gin

* fix

* Prepare for metapath sampler

This file is just for reviewing metapath sampling algorithm (Python version).

* Delete metapath_sampler

* Prepare for metapath sampler

This file is just for reviewing metapath sampling algorithm (Python code).

* Add files via upload

* Create metapath2vec.md

* Add files via upload

* Delete data_handler.py

* Delete word2vec.py

* Delete word_train.py

* Add files via upload

Metapath2vec implementations. Metapath2vec++ needs negative sampler optimization.

* Delete shuffle_training.py

* Delete test.py

* Add files via upload

* Delete sampler.py

* Delete metapath_sampler.md

* Add files via upload

* Update and rename shuffle_training.py to metapath2vec.py

* Update reading_data.py

* Update metapath2vec.md

* Update metapath2vec.md

* Update metapath2vec.md

* Update metapath2vec.md

* Update metapath2vec.md

* Create label 2

* Delete label 2

* Create testing.md

* Add files via upload

* Create sample.md

* Add files via upload

* Delete sampler.py

* Add files via upload

* Delete googlescholar.8area.author.label.txt

* Delete googlescholar.8area.venue.label.txt

* Delete testing.md

* Delete id_author.txt

* Delete id_conf.txt

* Delete paper.txt

* Delete paper_author.txt

* Delete paper_conf.txt

* Delete sample.md

* Delete sampler.py

* Add files via upload

* Add files via upload

* Add files via upload

* Delete reading_data.py

* Add files via upload

* Add files via upload

* Delete metapath2vec.py

* Add files via upload

* Rename shuffle_training.py to metapath2vec.py

* Update metapath2vec.md

* Delete reading_data.py

* add comments and remov e commented codes

* upd

* fig edgebatch edges

* add test

* trigger

* Update README.md for pytorch PinSage example.

Add noting that the PinSage model example under
example/pytorch/recommendation only work with Python 3.6+
as its dataset loader depends on stanfordnlp package
which work only with Python 3.6+.

* Provid a frame agnostic API to test nn modules on both CPU and CUDA side.

1. make dgl.nn.xxx frame agnostic
2. make test.backend include dgl.nn modules
3. modify test_edge_softmax of test/mxnet/test_nn.py and
    test/pytorch/test_nn.py work on both CPU and GPU

* Fix style

* Delete unused code

* Make agnostic test only related to tests/backend

1. clear all agnostic related code in dgl.nn
2. make test_graph_conv agnostic to cpu/gpu

* Fix code style

* fix

* doc

* Make all test code under tests.mxnet/pytorch.test_nn.py
work on both CPU and GPU.

* Fix syntex

* Remove rand

* Add TAGCN nn.module and example

* Now tagcn can run on CPU.

* Add unitest for TGConv

* Fix style

* For pubmed dataset, using --lr=0.005 can achieve better acc

* Fix style

* Fix some descriptions

* trigger

* Fix doc

* Add nn.TGConv and example

* Fix bug

* Update data in mxnet.tagcn test acc.

* Fix some comments and code

* delete useless code

* Fix namming

* Fix bug

* Fix bug

* Add test code for mxnet TAGCov

* Update some docs

* Fix some code

* Update docs dgl.nn.mxnet

* Update weight init

* Fix

* initial commit

* second commit

* another commit

* change docstring

* migrating to dgl.nn

* fixes

* docs

* lint

* multiple fixes

* doc

* renaming nearest neighbor graph

* initial commit

* second commit

* another commit

* change docstring

* migrating to dgl.nn

* fixes

* docs

* lint

* multiple fixes

* doc

* GAT

* Fix mistake

* Fix

* hotfix

* Fix

* Fix

* Fix

* Fix

* Fix

* Fix

* Fix

* Update

* Update

* Update

* Fix style

* Hotfix

* Hotfix

* Hotfix

* Fix

* Fix

* Update

* CI trial

* Update

* Update

* Update

* jtnn model zoo

* poke ci

* fix line sep

* fix

* Fix import order

* fix render

* fix render

* revert

* fix

* Resolve conflict

* dix

* remove create_var

* refactor

* fix

* refactor

* readme

* format

* fix lint

* fix lint

* pylint

* lint

* fix lint

* fix lint

* add hint

* fix

* Remove vocab

* Add explanation for warning

* add directory

* Load model to cpu by default

* Update

* Make dataset framework agnostic

* Update

* Update

* Fix

* Remove unused import

* refactor

* upd mpnn

* gat

* upd

* upd sage

* upd

* upd

* upd

* upd

* upd

* add gmmconv

* upd ggnn

* upd

* upd

* upd

* upd

* add citation examples

* add README

* fix cheb

* improve doc

* formula

* upd

* trigger

* lint

* lint

* upd

* add test for transform

* add test

* check

* upd

* improve doc

* shape check

* upd

* densechebconv, currently not correct (?)

* fix cheb

* fix

* upd

* upd sgc-reddit

* upd

* trigger

* [Model] MPNN

* [Model] MPNN 🔨 reorganize the mpnn/sch/mgcn model & alchemy dataset

* [Model] MPNN alchemy dataloader refactoring

* [Model] Chem model zoo minor change

* [Model] Chem Model Zoo 🔥 remove old samples

* [Model Zoo] molecule regression minor change

* Fix dataset import

* Fix dataset import

* [Model Zoo] molecule regression test set

* [Model Zoo] molecule prediction MPNN model hyperparameter tuning

* [Model Zoo] molecule prediction ✏ mpnn performance update

* DGMG for molecule generation

* Fix CI check

* Fix for CI

* Trial for CI due to shared memory

* Update

* Better interface for dataset configuration

* Update

* Handle corner cases

* Update README

* Fix

* Fix

* Fix

* Fix

* Fix

* Refactor

* Fix

* Fix

* Fix

* Fix

* Fix

* Fix

* Update

* Fix

* Fix

* Fix

* Fix

* Fix

* Finallly

* upd

* fig edgebatch edges

* add test

* trigger

* Update README.md for pytorch PinSage example.

Add noting that the PinSage model example under
example/pytorch/recommendation only work with Python 3.6+
as its dataset loader depends on stanfordnlp package
which work only with Python 3.6+.

* Provid a frame agnostic API to test nn modules on both CPU and CUDA side.

1. make dgl.nn.xxx frame agnostic
2. make test.backend include dgl.nn modules
3. modify test_edge_softmax of test/mxnet/test_nn.py and
    test/pytorch/test_nn.py work on both CPU and GPU

* Fix style

* Delete unused code

* Make agnostic test only related to tests/backend

1. clear all agnostic related code in dgl.nn
2. make test_graph_conv agnostic to cpu/gpu

* Fix code style

* fix

* doc

* Make all test code under tests.mxnet/pytorch.test_nn.py
work on both CPU and GPU.

* Fix syntex

* Remove rand

* Add TAGCN nn.module and example

* Now tagcn can run on CPU.

* Add unitest for TGConv

* Fix style

* For pubmed dataset, using --lr=0.005 can achieve better acc

* Fix style

* Fix some descriptions

* trigger

* Fix doc

* rgcn module

* support id input

* WIP: model codes

* use faster index select

* dropout

* self loop

* WIP: link prediction

* fix lint

* WIP: docs

* docstring

* docstring

* merge two child classes

* mxnet rgcn module

* fix lint

* fix lint

* fix rename bug

* add uniform edge sampler

* fix fn name

* docstring

* fix mxnet rgcn module

* fix mx rgcn

* enable test on cuda

* Add early stop

* add mxnet version

* Poke ci

Add noting that the PinSage model example under
example/pytorch/recommendation only work with Python 3.6+
as its dataset loader depends on stanfordnlp package
which work only with Python 3.6+.